Low Measurement Noise Research Articles

EVALUATING GEOMETRY OF AN INDOOR SCENARIO WITH OCCLUSIONS BASED ON TOTAL STATION MEASUREMENTS OF WALL ELEMENTS

Abstract. Scan2BIM approaches, i.e. the automated reconstruction of building models from point cloud data, is typically evaluated against the same point clouds which are used as input for the reconstruction process. In doing so, the point clouds are often used as ground truth without considering their own inaccuracies. Thus, in this research, we investigate the manual creation of an accurate ground truth, with a process which takes into account the measurement accuracy as well as the modeling accuracy. Therefore we created a ground truth to an existing laser scan data with a total station, based on the assumption that a total station generally measures points more reliably. In addition, a manual selection and classification of points on the wall surfaces during the measurement, serves a reliable detection of the walls via plane fitting. This allows for the creation of a more reliable ground truth, which is determined by the intersection of the planes from corners and edges. The ground truth is aligned parallel to the axes of a local coordinate system. From MLS and TLS point clouds of the same building area, walls are manually classified and corners and edges are determined in a similar way to the total station. These TLS and MLS corners are registered to this ground truth using least squares optimisation at the vertices. The transformation thus determined is used to transform the laser scanning point clouds as well. The resulting errors in the corners and the whole point cloud are evaluated. We conclude that the standard deviation of wall surfaces alone isn’t sufficient to determine the quality of the reconstructed building model. Despite low measurement noise in single wall surfaces, deviations in the reconstructed room model may arise.

Automatic control of AC bridge-based capacitive strain sensor interface for wireless structural health monitoring

Thin film-based flexible strain sensors have various advantages for structural health monitoring (SHM) because of their capability to sustain large deformations and cover large area of structural surfaces, making them ideal candidates for applications to complex geometries and structural crack monitoring. The authors previously developed a flexible strain sensor technology based on a soft elastomeric capacitor (SEC) for SHM and investigated an alternating current (AC) bridge-based method to transform the strain-induced dynamic capacitance changes in the SEC into analog voltage signals. Previous experiments have verified the capability of the SEC and the AC bridge-based signal converter for structural strain sensing applications. However, careful manipulation requirement for precise AC-bridge balancing, signal amplification control, and shunt calibration limits its practical use for full-scale SHM in field conditions. This study addressed such limitations with critical updates in both hardware and software with fully automated features for high-sensitive capacitive strain sensing. Newly developed hardware and software are fully controlled with a low-cost microcontroller ATmega328p in an automated way. A series of lab tests validated the prototype's performances in the capacitive strain sensing, outperforming an off-the-shelf wired capacitance measurement system (about 34% lower measurement noise), and confirmed that automatic control features worked as designed.

Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions

The precise tracking of distance variations between two satellites in low Earth orbit can provide key data for the understanding of the Earth’s system, specifically on seasonal and sub-seasonal water cycles and their impact on water levels. Measured distance variations, caused by local variations in gravitational field, serve as inputs to complex gravity models with which the movement of water on the globe can be identified. Satellite missions GOCE (2009–2013) and GRACE (2002–2017) delivered a significant improvement to our understanding of spatial and temporal gravity variations. Since 2018, GRACE Follow-On has been providing data continuity and features for the first time through the use of a laser interferometer as the technology demonstrator, in addition to a microwave ranging system as the main instrument. The laser interferometer provides an orders-of-magnitude lower measurement noise, and thereby could enable a significant improvement in the measurement of geoids if combined with suitable improvements in auxiliary instrumentation and Earth system modelling. In order to exploit the improved ranging performance, the ESA is investigating the design of a ‘Next Generation Gravity Mission’, consisting of two pairs of satellites with laser interferometers, improved accelerometers and improved platform performance. In this paper, we present the current design of the laser interferometer developed by us, the development status of the individual instrument units and the options available.

GPS Spoofing Detection and Mitigation for Drones Using Distributed Radar Tracking and Fusion

In today’s world, Global positioning system (GPS)-based navigation is inexpensive for providing position, velocity, and time (PVT) information. GPS receivers are widely used on unmanned aerial vehicles (UAVs), and these targets are vulnerable to deliberate interference such as spoofing. In this paper, GPS spoofing detection and mitigation for UAVs are proposed using distributed radar ground stations equipped with a local tracker. In the proposed approach, UAVs and local trackers are linked to the fusion node. The UAVs estimate their position and covariance using the extended Kalman filter framework and send it to a fusion node as primary data. Simultaneously, the time-varying kinematics of the UAVs are estimated using the extended Kalman filter and global nearest neighbor association tracker frameworks, and this data is transmitted to the central fusion node as secondary data. A track-to-track association is proposed to detect spoofing attacks using available primary and secondary data. After detecting the spoofing attack, the secondary data is subjected to a correlation-free fusion. We propose using this fused state as a control input to the UAVs to mitigate the spoofing attack. The spoofing scenario results show that using the predicted fusion state provides the same accuracy as a GPS receiver in a clean environment. Furthermore, because the innovation is calculated using the predicted fused state, there is no effect on the number of satellite signals on PRMSE. Additionally, in terms of PRMSE, radars with low measurement noise outperform radars with high measurement noise. The proposed algorithm is best suited for use in drone swarm applications.

On practical solutions of series elastic actuator control in the context of active disturbance rejection

AbstractIn this paper, customized series elastic actuator (SEA) force and position control solutions are proposed to achieve high performance while keeping the bandwidth low. In the context of active disturbance rejection, the higher the bandwidth of extended state observer, the more robust and accurate the closed‐loop performance. However, high bandwidth requires high sampling frequency and low measurement noise, leading to higher cost in practice. It is shown in this paper that the bandwidth can be lowered without sacrificing performance by incorporating additional information into the design, including not only the entire known SEA model information but also angle measurements of both the motor and the load. In addition, this allows both matched and mismatched disturbances, at the motor and the load side respectively, to be estimated and mitigated in real time, thus making the design more robust. Simulation results verify the effectiveness and robustness of the proposed approach.

Application of the MEMS Accelerometer as the Position Sensor in Linear Electrohydraulic Drive.

Various distance sensors are used as measuring elements for positioning linear electrohydraulic drives. The most common are magnetostrictive transducers or linear variable differential transformer (LVDT) sensors mounted inside the cylinder. The displacement of the actuator’s piston rod is proportional to the change in the value of the current or voltage at the output from the sensor. They are characterized by relatively low measurement noise. The disadvantage of presented sensors is the need to mount them inside the cylinders and the high price. The article presents preliminary research on the replacement of following sensors and the use of a microelectromechanical system (MEMS) accelerometer as a measuring element in the electrohydraulic drive control system. The control consisted of two phases: at first, the signal from the acceleration sensor was analyzed during the actuator movement, based on the value determined from the simplified model implemented on the controller. In the range of motion in which the dynamics were the lowest, the signal was integrated and the obtained value was used in the second phase of motion. In the correction phase, a new set point was determined. Conducting the research required building a dedicated research stand. The author conducted the simulation and experimental research.

2D Material-Enabled Nanomechanical Bolometer.

We here describe a novel type of long-wavelength radiation detector that measures illumination intensity at room temperature through mechanical transduction. Compared to semiconductor-based bolometers, our nanomechanical detector exhibits low measurement noise and is inherently transparent and flexible. The presented solid-state device is based on a 2D-material film that acts as radiation absorber and detector of mechanical strain at the substrate-absorber interface. Optimization of the 2D material properties and realization of a novel edge-on device geometry combines unprecedented detectivity of 3.34 × 108 cm Hz1/2 W-1 with micrometer-scale spatial resolution. The observed combination of superior performance with the facile and scalable fabrication using only liquid processes shows the potential of the presented detector for future ubiquitous and wearable electronics.

A Laser Interferometer Prototype with Pico-Meter Measurement Precision for Taiji Space Gravitational Wave Detection Missionin China

The laser interferometer is one of the most important key technologies for the space gravitational wave detection. A laser interferometer prototype with pico-meter measurement precision for Taiji mission in China is presented in the paper. The results showed that the path-length measurement precision reached 5 pm/√Hz within the frequency range of 10 mHz – 1 Hz by improving the temperature fluctuation noise and electronic readout noise of the laser interferometer, which meets the requirement of Taiji Pathfinder mission inside the frequency range of 1 mHz – 1 Hz under the weak-light condition. It would be a fine experimental platform for the key technologies demonstration including the laser pointing modulation and the laser phase-locking control to improve the precision for the low measurement noise.

Simultaneous dual-wavelength phase-shifting interferometry for surface topography measurement

To deal with vibration disturbance in interferometric measurement of surface topography, we present a simultaneous dual-wavelength phase-shifting interferometry (SDWPSI). SDWPSI collects phase-shifting interferograms of two wavelengths simultaneously with two cameras to accelerate data collection. The optical phases are retrieved from dual-wavelength interferograms with an iterative algorithm that is tolerant of phase-shifting error caused by vibration. And then the measured surface topography is reconstructed by fusing the phases of two wavelengths. Simulations and experiments verify that SDWPSI has a lower measurement noise than single-wavelength interferometry and extends the discontinuity limit to several micrometres. SDWPSI has advantages of high speed, vibration resistance, and low cost, and could be applied to in-workshop and on-machine measurement.

Simulating multiple faceted variability in single cell RNA sequencing

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.

Positioning of Geostationary Satellite by Radio Interferometry

There are several perturbations that force the satellite orbit to deviate from the elliptical form. To cope with these perturbations, station keeping is applied. For accurate station keeping, determination of the true satellite orbit is necessary. Radio interferometry is one of the techniques used for determining azimuth and elevation look angles of a satellite. In this paper, we introduce three structures based on interferometry for determining the view angles of satellites. The estimated angles are combined with range measurement for orbital positioning of geostationary (GEO) satellites. In the proposed interferometry structures, to acquire the required angular accuracy, the distance between antennas is set larger than the maximum half-wavelength, and the incurred ambiguities are resolved by utilizing properties of GEO satellites. For each of the proposed structures, maximum likelihood estimators are proposed. It is shown that, in low measurement noise cases, these proposed estimators are unbiased and their mean square error achieves the Cramer–Rao bound.

Effects of Dynamic Range and Sampling Rate of an Infrared Thermometer to the Accuracy of the Cloud Detection

Cloud detection using downwelling radiation measured by infrared thermometer (IRT) has been utilized for many applications. The current study investigates the effects of disparate IRT specifications, including the dynamic range and sampling rates on the performance of cloud detection, which utilizes the spectral and temporal characteristics of cloudy radiation. To analyze the effects, the detection algorithm that was prepared with and applied to the IRT data with different specifications is compared with reference data, a ceilometer, and micro-pulse lidar (MPL). The comparison results show that the low-altitude clouds are detected with a sufficient accuracy: better than 97% probability of detection (POD). This is due to the much warmer brightness temperature (Tb) of the low clouds compared with the clear sky in the atmospheric window region where the IRT measurement was made. Conversely, the high-altitude cold clouds are hard to detect with the spectral test due to the much-reduced Tb contrast between cloudy and clear sky. Thus, the algorithm performance is largely dependent on the performance of the temporal test. Since the lower measurement noise provides a better estimation of the temporal variability of clear sky Tb with less estimation uncertainty, the IRT data having a better noise performance shows a better POD value by as much as 52.2% compared with the MPL result. However, the improvement is realized only when the dynamic range of IRT covers sufficiently cold Tb, such as −100 °C.

Combined state and parameter identification of nonlinear structural dynamical systems based on Rao-Blackwellization and Markov chain Monte Carlo simulations

The problem of combined state and parameter estimation in nonlinear state space models, based on Bayesian filtering methods, is considered. A novel approach, which combines Rao-Blacicwellized particle filters for state estimation with Markov chain Monte Carlo (MCMC) simulations for parameter identification, is proposed. In order to ensure successful performance of the MCMC samplers, in situations involving large amount of dynamic measurement data and (or) low measurement noise, the study employs a modified measurement model combined with an importance sampling based correction. The parameters of the process noise covariance matrix are also included as quantities to be identified. The study employs the Rao-Blackwellization step at two stages: one, associated with the state estimation problem in the particle filtering step, and, secondly, in the evaluation of the ratio of likelihoods in the MCMC run. The satisfactory performance of the proposed method is illustrated on three dynamical systems: (a) a computational model of a nonlinear beam moving oscillator system, (b) a laboratory scale beam traversed by a loaded trolley, and (c) an earthquake shake table study on a bending-torsion coupled nonlinear frame subjected to uniaxial support motion. (C) 2017 Elsevier Ltd. All rights reserved.

Regularization of the matrix Riccati equation in optimal estimation problem with low measurement noise

We explore the possibility of applying the method of order reduction of optimal estimation problem for singularly perturbed systems with low measurement noise. It is shown that matrix Riccati equation for the Kalman-Bucy filter has a periodic solution. An optimal filter is constructed for a dynamic model of a crank mechanism.

Tomographic image reconstruction via estimation of sparse unidirectional gradients

Since computed tomography (CT) was developed over 35 years ago, new mathematical ideas and computational algorithms have been continuingly elaborated to improve the quality of reconstructed images. In recent years, a considerable effort can be noticed to apply the sparse solution of underdetermined system theory to the reconstruction of CT images from undersampled data. Its significance stems from the possibility of obtaining good quality CT images from low dose projections. Among diverse approaches, total variation (TV) minimizing 2D gradients of an image, seems to be the most popular method. In this paper, a new method for CT image reconstruction via sparse gradients estimation (SGE), is proposed. It consists in estimating 1D gradients specified in four directions using the iterative reweighting algorithm. To investigate its properties and to compare it with TV and other related methods, numerical simulations were performed according to the Monte Carlo scheme, using the Shepp-Logan and more realistic brain phantoms scanned at 9–60 directions in the range from 0 to 179°, with measurement data disturbed by additive Gaussians noise characterized by the relative level of 0.1%, 0.2%, 0.5%, 1%, 2% and 5%. The accuracy of image reconstruction was assessed in terms of the relative root-mean-square (RMS) error. The results show that the proposed SGE algorithm has returned more accurate images than TV for the cases fulfilling the sparsity conditions. Particularly, it preserves sharp edges of regions representing different tissues or organs and yields images of much better quality reconstructed from a small number of projections disturbed by relatively low measurement noise.

Rigorous Strip Adjustment of UAV-based Laserscanning Data Including Time-Dependent Correction of Trajectory Errors

A new generation of laser scanners mounted on Unmanned Aerial Vehicles (uavs) have the potential to provide highquality point clouds of comparatively small areas (a few hectares). The high maneuverability of the uavs, a typically large field of view of the laser scanners, and a comparatively small measurement range lead to point clouds with very high point density, less occlusions, and low measurement noise. However, due to the limited payload of uavs, lightweight navigation sensors with a moderate level of accuracy are used to estimate the platform’s trajectory. As a consequence, the georeferencing quality of the point clouds is usually sub-optimal; for this, strip adjustment can be performed. The main goal of strip adjustment is to simultaneously optimize the relative and absolute orientation of the strip-wise collected point clouds. This is done by fully re-calibrating the laser scanning system and by correcting systematic measurement errors of the trajectory. In this paper, we extend our previous work on the topic of strip adjustment by the estimation of time-dependent trajectory errors. The errors are thereby modelled by natural cubic splines with constant segment length in time domain. First results confirm the suitability of this flexible correction model by reducing the relative and absolute strip discrepancies to 1.38cm and 1.65cm, respectively.

A modified un-combined model to improve the performance of precise point positioning: model and test results

Precise point positioning (PPP) can achieve high-accuracy point solution using un-differenced code and carrier phase observations. Many PPP models have a higher measurement noise and multipath effects, resulting in a long time to obtain sub-decimeter to centimeter positioning accuracy during a short duration. This paper will address a modified un-combined (MUC) model based on phase–phase geometry-free combination in addition to code–phase ionosphere-free combination and original carrier phase observations. The observation system composed by the new combinations has a lower measurement noise and orbit error level compared with the un-combined (UC) model, University of Calgary (UofC) model and standard un-differenced ionosphere-free combined (UD) model. Moreover, it can capture the changing characteristics of atmospheric delays between epochs as constraints to accelerate the filter convergence. We use the data sets from about 114 international GNSS service reference stations to analyze the performance of the MUC model. Numerical results show that the MUC model generally yields the best performance when the observation duration is short. For the 0.5 h duration observation data sets, about 70.2 % of all 6912 convergence tests are convergent, which is an increase by 19.2, 2.8 and 49.3 % points compared with the UC, UofC and UD models, respectively. For the common convergence parts of all the test data sets, the mean convergence time of the MUC model is significantly reduced by 22.9, 9.6 and 35.3 %, and the percentage of the 2D position biases within 0–5 cm is increased by 16.0, 3.4 and 55.0 %, respectively, compared with the UC, UofC and UD models. Therefore, the proposed PPP model is more beneficial for the PPP user to quickly obtain sub-decimeter to centimeter positioning accuracy during a short duration.

Measurement Signal Quality Assessment on All Available and New Signals of Multi-GNSS (GPS, GLONASS, Galileo, BDS, and QZSS) with Real Data

Global Navigation Satellite Systems (GNSS) Carrier Phase (CP)-based high-precision positioning techniques have been widely used in geodesy, attitude determination, engineering survey and agricultural applications. With the modernisation of GNSS, multi-constellation and multi-frequency data processing is one of the foci of current GNSS research. The GNSS development authorities have better designs for the new signals, which are aimed for fast acquisition for civil users, less susceptible to interference and multipath, and having lower measurement noise. However, how good are the new signals in practice? The aim of this paper is to provide an early assessment of the newly available signals as well as assessment of the other currently available signals. The signal quality of the multi-GNSS (GPS, GLONASS, Galileo, BDS and QZSS) is assessed by looking at their zero-baseline Double Difference (DD) CP residuals. The impacts of multi-GNSS multi-frequency signals on single-epoch positioning are investigated in terms of accuracy, precision and fixed solution availability with known short baselines.

The estimation of time-invariant parameters of noisy nonlinear oscillatory systems

The inverse problem of estimating time-invariant (static) parameters of a nonlinear system exhibiting noisy oscillation is considered in this paper. Firstly, a Markov Chain Monte Carlo (MCMC) simulation is used for the time-invariant parameter estimation which exploits a non-Gaussian filter, namely the Ensemble Kalman Filter (EnKF) for state estimation required to compute the likelihood function. Secondly, a recently proposed Particle Filter (PF) (that uses the EnKF for its proposal density for the state estimation) has been adapted for combined state and parameter estimation. Numerical illustrations highlight the strengths and limitations of the MCMC, EnKF and PF algorithms for time-invariant parameter estimation. For low measurement noise and dense measurement data, the performances of the MCMC, EnKF and PF algorithms are comparable. For high measurement noise and sparse observational data, the EnKF fails to provide accurate parameter estimates. Hence the adapted PF algorithm becomes necessary in order to obtain parameter estimates comparable in accuracy to the MCMC simulation with EnKF. It highlights the fact that the augmented state space model for the combined state and parameter estimation contains stronger nonlinearity than the original state space model. Hence the EnKF effectively handles the state estimation of the original state space model, but it fails for the combined state and parameter estimation using the augmented system. The effectiveness of the EnKF for the state estimation is therefore leveraged in the MCMC simulation for the time-invariant parameter estimation. In order to obtain accurate parameter estimates using the augmented system, the adapted PF becomes necessary to match the parameter estimates obtained using the MCMC simulation complemented by EnKF for likelihood function computation.

Enabling low-noise null-point scanning thermal microscopy by the optimization of scanning thermal microscope probe through a rigorous theory of quantitative measurement.

The application of conventional scanning thermal microscopy (SThM) is severely limited by three major problems: (i) distortion of the measured signal due to heat transfer through the air, (ii) the unknown and variable value of the tip-sample thermal contact resistance, and (iii) perturbation of the sample temperature due to the heat flux through the tip-sample thermal contact. Recently, we proposed null-point scanning thermal microscopy (NP SThM) as a way of overcoming these problems in principle by tracking the thermal equilibrium between the end of the SThM tip and the sample surface. However, in order to obtain high spatial resolution, which is the primary motivation for SThM, NP SThM requires an extremely sensitive SThM probe that can trace the vanishingly small heat flux through the tip-sample nano-thermal contact. Herein, we derive a relation between the spatial resolution and the design parameters of a SThM probe, optimize the thermal and electrical design, and develop a batch-fabrication process. We also quantitatively demonstrate significantly improved sensitivity, lower measurement noise, and higher spatial resolution of the fabricated SThM probes. By utilizing the exceptional performance of these fabricated probes, we show that NP SThM can be used to obtain a quantitative temperature profile with nanoscale resolution independent of the changing tip-sample thermal contact resistance and without perturbation of the sample temperature or distortion due to the heat transfer through the air.